3. Data, Science & AI

Creates advanced Excel pivot tables with calculated fields and slicers for data summarization and interactive dashboard creation.

Manages multi-agent coordination for Claude Code, handling installation, workspace setup, agent lifecycle, and CLI operations.

Automates creation of Apache Airflow DAGs for data pipeline orchestration, enabling efficient workflow management.

Configures TensorFlow Serving for deploying machine learning models in production environments.

Analyzes text to classify emotional tone as positive, negative, or neutral using sentiment analysis techniques.

Enables setup and coordination of multi-agent AI systems with intelligent routing and handoffs across providers like OpenAI and Anthropic.

Automates generation and analysis of machine learning model evaluation metrics during training workflows.

Optimizes OpenRouter API response times and throughput by adjusting configuration and usage patterns to reduce latency.

Automates time series decomposition to isolate trends, seasonality, and residuals in data analytics workflows.

Generates videos from text prompts using Kling AI, enabling rapid video creation from descriptive inputs.

Analyzes A/B test operations for data-driven insights. Auto-activating tool triggered by phrases like 'ab test analyzer'.

Evaluates machine learning model performance using comprehensive metrics for accuracy, validation, and testing in response to user requests.

Automates generation of TorchServe configuration files for efficient machine learning model deployment.

Provides index design and optimization guidance to improve database query performance and data retrieval efficiency.

Automatically extracts metadata from data sources to enhance data pipeline efficiency and preparation for analysis.

Auto-activating tool that generates model explanations during machine learning training to improve transparency and interpretability.

Creates a minimal working example for Lindy AI agent development, ideal for initial integration, setup testing, and learning basic API patterns.

Automates activation of Google Vertex AI multimodal operations based on contextual triggers, enabling seamless AI-driven media processing.

Creates machine learning models in Google BigQuery using SQL-based operations for data analysis and prediction.

Automates ML experiment tracking setup with MLflow or W&B, providing configuration and code snippets for logging parameters, metrics, and artifacts.

Creates and manages Optuna studies for hyperparameter optimization in machine learning model training.

Automates generation of dbt models for data transformation pipelines in data warehouses.

Analyzes cryptocurrency market data including whale movements, token flows, and network activity for real-time market insights.

Builds personalized recommendation systems using collaborative, content-based, or hybrid filtering by analyzing user preferences and interaction data.